Blasting is used in the recovery of mineral resources, including in surface mining and quarrying for rock fragmentation and displacement of the broken rock. However, if the process is not organized properly, the rock fragments may be thrown beyond the expected limits that may result in accidents including critical injuries, or fatalities. An efficient preventive measure is the blasting process improvement that relies on the on-site video recording, storage, automatic analysis and classification of the recorded blasts followed by the automatic generation of reports. Although automatic video analysis is commonly used in many application areas such as video surveillance, traffic monitoring and robotics, the algorithms that have been developed to recognize and track solid objects with well defined shapes, textures and colors, are not efficient to deal with blasts which by their nature have dynamically changing shapes, colors and textures. Moreover, strong camera vibration caused by blasts will badly affect performance of conventional computer vision algorithms.
Scott G. Giltner, Paul N. Worsey, Blast Monitoring using High Speed Video Research Equipment, International Society of Explosives Engineers, 2000, pp. 178-190 describes a blast examination method that uses a motion analysis system designed to research various high speed processes and a computer program. To reduce the vibration effect, the camera tripod is set on several inches of foam rubber. The camera set up also requires several blast markers which are used to convert the camera units of measurements into feet. Trajectories and velocities of particles are measured manually through tracking them with the reticle. The reticle coordinates are used to plot out the trajectory of the particles for further analysis. Thus, the process of blast analysis and parameter estimation is not fully automatic and even manually it is not possible without the markers. Although efficiency of the camera vibration reduction is not reported, the use of a foam rubber mat placed under the camera tripod can hardly eliminate strong vibration caused by blasts. Also, it makes the camera set up process less practical for the industrial use.
Qian Liu, The Application of High Speed Video System for Blasting Research, International Society of Explosives Engineers, 2000, pp. 359-373 describes a blast monitoring method that uses a high speed video system Motion Analyzer that is capable of recording at various frame rates, and is incorporated herein by reference. Recorded videos are used to draw contours of stemming ejections. The blast analysis process is not automatic and therefore it is time consuming.
J. Gubbi, et al., Smoke detection in video using wavelets and support vector machines, Fire Safety Journal, 2009 describes a video based fire detection method that uses multi level Wavelet decomposition of video frames. Each video frame is subdivided into blocks of 32×32 pixels, and is incorporated herein by reference. Then, every block is checked for the presence of smoke. The architecture is based on a standard pattern recognition approach with preprocessing, feature extraction and classification sub-units with training and testing phases. The proposed method can detect smoke, but the described block based solution cannot be used for tracking of high speed particles. Also, the described method cannot differentiate clouds of smoke produced by explosive materials from dust clouds.
R. J. Ferrari, H. Zhang, C. R. Kube, Real-time detection of steam in video images, Pattern Recognition 40 (3) (2007) 1148-1159 describes a block based image processing method for steam detection in oil sand mines, and is incorporated herein by reference. The method uses Wavelet transform and Hidden Markov Model for feature extraction and support vector machines for classification. The solution is based on the assumption that the presence of steam acts as a blurring process, which changes the local texture pattern of an image while reducing the amount of details. In general, this assumption is suitable for only very limited types of blasts.
G Bradski, A. Kaehler, Learning OpenCV, O'Relly, 2008, pp. 276 and 278-285 is hereby incorporated herein by reference.